Sriraam Natarajan's Books

Statistical Relational Artificial Intelligence

Logic, Probability, and Computation

By: Luc De Kang,Kristian Chen,Sriraam Yu,David Genesereth

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Statistical Relational Artificial Intelligence

Logic, Probability, and Computation

By: Luc De Kang,Kristian Chen,Sriraam Yu,David Genesereth

An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

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Page Count

175

Publisher

Springer Nature

Published Date

ISBN 10

3031015746

ISBN 13

9783031015748

Boosted Statistical Relational Learners

From Benchmarks to Data-Driven Medicine

By: Sriraam Natarajan,Kristian Kersting,Tushar Khot,Jude Shavlik

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Boosted Statistical Relational Learners

From Benchmarks to Data-Driven Medicine

By: Sriraam Natarajan,Kristian Kersting,Tushar Khot,Jude Shavlik

This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.

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Springer

Published Date

ISBN 10

3319136445

ISBN 13

9783319136448

Boosted Statistical Relational Learners

From Benchmarks to Data-Driven Medicine

By: Sriraam Natarajan,Kristian Kersting,Tushar Khot,Jude Shavlik

Write a review Read reviews Take a Quiz Solve Book Puzzle

Boosted Statistical Relational Learners

From Benchmarks to Data-Driven Medicine

By: Sriraam Natarajan,Kristian Kersting,Tushar Khot,Jude Shavlik

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N/A

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Solve jigsaw puzzle

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Publisher

Springer

Published Date

ISBN 10

3319136445

ISBN 13

9783319136448

Book's by Sriraam Natarajan

Statistical Relational Artificial Intelligence

Statistical Relational Artificial Intelligence

Boosted Statistical Relational Learners

Boosted Statistical Relational Learners

Boosted Statistical Relational Learners

Boosted Statistical Relational Learners

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